Function run_inference

lora_ft_webui.py:255–371

(text, prompt_wav, prompt_text, lora_selection, cfg_scale, steps, seed, pretrained_path=None)

def run_inference(text, prompt_wav, prompt_text, lora_selection, cfg_scale, steps, seed, pretrained_path=None):
    # 如果选择了 LoRA 模型且当前模型未加载，尝试从 LoRA config 读取 base_model
    if current_model is None:
        # 优先使用用户指定的预训练模型路径
        base_model_path = pretrained_path if pretrained_path and pretrained_path.strip() else default_pretrained_path

        # 如果选择了 LoRA，尝试从其 config 读取 base_model
        if lora_selection and lora_selection != "None":
            full_lora_path = os.path.join("lora", lora_selection)
            lora_config_file = os.path.join(full_lora_path, "lora_config.json")

            if os.path.exists(lora_config_file):
                try:
                    with open(lora_config_file, "r", encoding="utf-8") as f:
                        lora_info = json.load(f)
                    saved_base_model = lora_info.get("base_model")

                    if saved_base_model:
                        # 优先使用保存的 base_model 路径
                        if os.path.exists(saved_base_model):
                            base_model_path = saved_base_model
                            print(f"Using base model from LoRA config: {base_model_path}", file=sys.stderr)
                        else:
                            print(f"Warning: Saved base_model path not found: {saved_base_model}", file=sys.stderr)
                            print(f"Falling back to default: {base_model_path}", file=sys.stderr)
                except Exception as e:
                    print(f"Warning: Failed to read base_model from LoRA config: {e}", file=sys.stderr)

        # 加载模型
        lora_to_load = lora_selection if lora_selection and lora_selection != "None" else None
        try:
            print(f"Loading base model: {base_model_path}", file=sys.stderr)
            load_model(base_model_path, lora_to_load)
            if lora_to_load:
                print(f"Model loaded with LoRA: {lora_selection}", file=sys.stderr)
        except Exception as e:
            error_msg = f"Failed to load model from {base_model_path}: {str(e)}"
            print(error_msg, file=sys.stderr)
            return None, error_msg
        lora_just_loaded = lora_to_load
    else:
        lora_just_loaded = None

    # Handle LoRA hot-swapping
    assert current_model is not None, "Model must be loaded before inference"
    if lora_selection and lora_selection != "None":
        full_lora_path = os.path.join("lora", lora_selection)

        if lora_just_loaded != lora_selection:
            new_lora_config, new_base_model = load_lora_config_from_checkpoint(full_lora_path)
            current_r = current_model.tts_model.lora_config.r if current_model.tts_model.lora_config else None
            new_r = new_lora_config.r if new_lora_config else None

            if new_r is not None and current_r is not None and new_r != current_r:
                print(f"LoRA rank mismatch (model r={current_r}, checkpoint r={new_r}), reloading...", file=sys.stderr)
                reload_base = (
                    new_base_model if new_base_model and os.path.exists(new_base_model)
                    else (pretrained_path if pretrained_path and pretrained_path.strip() else default_pretrained_path)